This invention relates generally to systems for collecting information regarding operating characteristics of machine components for diagnostic purposes and more specifically to algorithms used with accelerometers that track acceleration of machine components, the algorithms useable to increase accelerometer accuracy, minimize the effects of noise and to generate relatively accurate velocity and position information as well as state information for the components based on the acceleration values and known information regarding component operating characteristics (e.g., operating states, possible state transitions, condition-state singularities, etc.).
Many automated systems include mechanical machine components that are controlled to move with respect to each other to perform a task. For example, in the case of automated manufacturing, machines and machine components are routinely mounted for sliding motion with respect to other machines and components to perform related tasks. As another instance, elevator cars are typically mounted on tracks for movement between floors of buildings and elevator doors are likewise mounted on tracks for movement between open and closed positions. Hereinafter, while the present invention is applicable to many different applications where one machine component moves with respect to others, in order to simplify this explanation, the problems solved by the present invention and the inventive aspects themselves will be described in the context of an exemplary elevator including a car and a car door where the elevator is mounted for movement between ten floors of a building and where the door is mounted for movement between open and closed positions, the door including a leading edge that travels at least in part along a door sash within the door opening when moving from a closed position into an open position.
When designing any elevator and elevator control system, certain criteria are important. To this end, some important criteria include smoothness of operation, robustness and operating speed. With respect to smoothness of operation, elevator car movements should be as smooth as possible to avoid injuring passengers inside the elevator car and to minimize the feeling of movement thereby enhancing passenger comfort. Thus, for instance, during normal elevator operation, to move an elevator car from a stationary position at an initial floor to a stationary position at a final floor, car velocity should be increased gradually up to a constant traveling velocity and, prior to reaching the final floor, the velocity should be ramped down gradually.
With respect to robustness, while elevator components are often designed to be extremely durable while operating under various conditions, components are typically designed to operate best under a specific set of circumstances. To this end, in the case of an elevator car door, wear and tear can be minimized by controlling door movements so that the door stops and commences movement relatively smoothly. Similarly, smooth car movements typically prolong the useful life of a car and supporting components. Control algorithms can be designed to facilitate sustainable car and door operations by commanding smooth and essentially ideal movements where car and door velocities ramp up and down along ideal curves.
With respect to operating speed, elevator components should move as quickly as possible without affecting the riding comfort of passengers therein and without unduly affecting wear and tear on components. For example, in the case of the elevator car, while velocity should ramp up and down at the beginning and end of a travel cycle, the ramp phases of travel should be as short as possible and the constant velocity phase should have a velocity as high as possible without affecting ride comfort or component durability. Similarly, in the case of an elevator door, the up and down velocity ramp phases should be as steep as possible without unduly adversely affecting component durability and passenger safety.
While ideal or optimal control algorithms have been developed for specific elevator configurations that properly balance each of the smoothness, robustness and speed considerations, unfortunately, over time all mechanical components experience wear and tear that affect operating characteristics and that therefore require maintenance or replacement. Hereinafter the general condition of elevator components or systems will be referred to as the “health” of the component or system unless indicated otherwise.
One way to monitor elevator component and system health has been to employ servicemen to periodically visit elevators and to manually test operating characteristics to identify any tell tail signs of impending maintenance problems. Systems that rely on service visits to evaluate system health have several shortcomings. First, service visits are relatively expensive as servicemen typically require specialized training in all aspects of system operation. In addition, service visits are usually expensive as, most of the time, a system checkup will reveal that the elevator system is healthy and that no maintenance is required.
Second, while service visits can be used to determine system health at the time of the visit, the information generated during a service visit represents only a snap shot in time of system operation which may not reveal operational nuances that occur at other operating times and from which long term operating trends cannot be identified.
Third, in some cases operating characteristics can degrade relatively quickly and, in any event, between health checkups. Here, where operating characteristics degrade rapidly prior to a next checkup, degrading operation may cause excessive and undue damage to components as well as noticeably adversely affect elevator operating characteristics such as smoothness and speed.
One solution to the diagnostic problem described above has been to provide a diagnostic assembly including system sensors, a processor and a database wherein the processor routinely monitor system operating characteristics via the sensors and stores the characteristics in the database. Thereafter, the processor or another processor may be programmed to process and analyze the stored data to identify any nuances that may indicate degradation in system health and to provide warnings when a system should be services. While various types of data can be monitored and stored for subsequent analysis, some particularly useful types of information include velocities of component travel, component positions and, in at least some cases, component operating states (e.g., in the case of an elevator car, standing, accelerating, constant velocity, decelerating, emergency stop).
At least some diagnostic assemblies include one or more accelerometers to generate the data required to monitor system health. For instance, a first accelerometer may be mounted to an elevator car to monitor elevator car acceleration and to generate acceleration values indicative thereof while a second accelerometer may be mounted to or adjacent a car door to monitor door acceleration and generate acceleration values indicating door acceleration. Here, door velocity can be determined by integrating the acceleration values and position can be determined by integrating the velocity values.
Exemplary accelerometers measure acceleration and generate an output voltage u that is proportional thereto. Here, the output voltage u is related to the actual component acceleration by an accelerometer gain value gh (i.e., u=agh). Thus, to identify an instantaneous acceleration value using output voltage u, a processor receiving value u runs software and divides the output voltage value u (i.e., a=u/mc) by a modifier mc where modifier mc is set equal to gh.
While accelerometers can be used to generate useful information, it has been observed that typical acceleration values often include a large noise component which results in operating characteristic data that does not accurately reflect operation of the system. For instance, when an elevator car is stationary (i.e., the velocity is zero), often an accelerometer will nevertheless generate a noise signal that, when integrated, indicates at least some car velocity and hence a changing car position—clearly an erroneous determination. Because integrating processes to identify velocity and position assume initial velocities and positions, errors due to noise accumulate and become greater over time.
In addition, unfortunately, accelerometer gain gh has been known to change with temperature, long term use, etc. and therefore, while modifier value mc used by the software program run by the processor may initially be accurate (i.e., mc=gh initially), over time, as gain gh changes (i.e., mc≠gh) and the accuracy of the acquired signal is reduced.
Thus, it would be advantageous to have an accelerometer based system that employs algorithms useable to increase accelerometer accuracy, minimize the effects of noise and to generate relatively accurate velocity and position information as well as state information for machine components based on the acceleration values as well as minimal information regarding component operating characteristics. In addition, it would be advantageous to have a system that automatically follows changes in accelerometer gains when accuracy drifts.